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Econometrics Toolbox

Model and analyze financial and economic systems using statistical time series methods

Econometrics Toolbox™ provides functions and interactive workflows for analyzing and modeling time series data. It offers a wide range of visualizations and diagnostics for model selection, including tests for autocorrelation and heteroscedasticity, unit roots and stationarity, cointegration, causality, and structural change. You can estimate, simulate, and forecast economic systems using a variety of modeling frameworks that can be used either interactively, using the Econometric Modeler app, or programmatically, using functions provided in the toolbox. These frameworks include regression, ARIMA, state-space, GARCH, multivariate VAR and VEC, and switching models. The toolbox also provides Bayesian tools for developing time-varying models that learn from new data.

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Learn the basics of Econometrics Toolbox

Data Preprocessing

Format, plot, and transform time series data

Model Selection

Specification testing and model assessment

Time Series Regression Models

Bayesian linear regression models and regression models with nonspherical disturbances

Conditional Mean Models

Autoregressive (AR), moving average (MA), ARMA, ARIMA, ARIMAX, and seasonal models

Conditional Variance Models

GARCH, exponential GARCH (EGARCH), and GJR models

Multivariate Models

Cointegration analysis, vector autoregression (VAR), vector error-correction (VEC), and Bayesian VAR models

Regime-Switching Models

Discrete-state threshold-switching dynamic regression, discrete-time Markov chain, and Markov-switching dynamic regression models

State-Space Models

Continuous state-space Markov processes characterized by state and observation equations